1. Field of the Invention
The present invention relates to a data learning apparatus, a data learning program and a computer-readable recording medium carrying a data learning program. More specifically, the present invention relates to an apparatus, a program and a computer-readable recording medium for learning data by supervised learning for deriving a self-organizing map to be used in processing of determining contents of information such as determination of a meaning of an image. Moreover, the present invention also relates to an apparatus for determining a meaning of a target image or an image region by use of a self-organizing map.
2. Description of the Related Art
As a method for sorting information into classes, for searching information and the like, a method utilizing a self-organizing map has recently started to be used as an alternative to a conventional clustering method and the like.
The self-organizing map is a map in which a plurality of multidimensional vectors are spatially arranged. Each of the multidimensional vectors is a vector having as components a plurality of parameters each indicating one feature of reference data (hereinafter referred to as a “reference feature vector”) On this self-organizing map, preliminary learning of many pieces of sample data allows reference feature vectors similar to each other to be arranged at positions close to each other.
Generally, learning described above is “unsupervised learning.” This learning is performed by repeating the following steps. First, an initial self-organizing map is prepared, in which a plurality of random reference feature vectors are distributed. Thereafter, a feature vector as a learning target is read and a winner vector having the highest similarity to the feature vector is retrieved from the reference feature vectors on the self-organizing map. Subsequently, the winner vector and the reference feature vectors distributed in the vicinity of the winner vector are modified so as to increase the similarity thereof to the feature vector as the learning target. By use of the self-organizing map obtained after performing the unsupervised learning, as to many pieces of input information, a winner vector having the highest similarity to vectors indicating features of the respective pieces of input information can be searched for on the self-organizing map. Thereafter, the respective pieces of input information can be mapped in a position of the winner vector on the self-organizing map. Thus, display of information listed in a two-dimensional map or the like is made possible (for example, see Japanese Unexamined Patent Publication Nos. 2002-41545 and 2001-337953). In such a list display, information having similar features (for example, similar images, similar product information and the like) are arranged close to each other and displayed. Thus, it is easy to capture the information visually and to browse the information. Moreover, there has also been proposed a method for classifying or prioritizing input information in the following manner (for example, see Japanese Unexamined Patent Publication Nos. 2001-306612 and 2001-283184). In the self-organizing map obtained after performing the unsupervised learning, a plurality of island-shaped regions, in which vectors having particularly strong similarity are gathered, are defined as clusters. Accordingly, it is checked which one of the clusters a winner vector belongs to, the winner vector having the highest similarity to a vector indicating features of input information.
For the purpose of classification or display of information, it is only necessary to use a self-organizing map obtained by the unsupervised learning described above. However, in order to apply the map to content determining processing (class determining processing) for information, it is necessary that respective vector points on a self-organizing map obtained after learning are each associated with contents (classes) of information. For this, there has also been proposed a method of “modified counterpropagation (MCP)” or the like, in which “supervised learning” and the self-organizing map are combined together (for example, see Tokutaka et al., “Application of Self-Organizing Maps—Two-Dimensional Visualization of Multidimensional Information,” Kaibundo, 1999, pp. 1-19, 63-75). Specifically, in the supervised learning, data to be learned is previously divided into classes and it is known which one of the classes respective pieces of data belong to. In a learning step by use of this modified counterpropagation method, “frequency maps” having the same size as the self-organizing map are prepared for the respective classes. Accordingly, along with learning of the self-organizing map, the frequency map is created for each of the classes. The frequency map shows a frequency of appearance of winner vectors for feature vectors as learning targets, which belong to the class, at each of vector points or in the vicinity thereof on the self-organizing map. Thus, after the learning is finished, obtained are: a self-organizing map in which reference feature vectors similar to each other are arranged in positions close to each other; and a map of corresponding classes, showing information about the class most frequently appearing at each vector point on the self-organizing map. Therefore, by use of the self-organizing map obtained after the learning, a winner vector having the highest similarity to a vector indicating features of input information can be searched for on the self-organizing map and a corresponding point of a vector point of the winner vector on the map of corresponding classes can be referred to. Thus, it is made possible to determine which one of the classes the input information belongs to.
Meanwhile, as another method which can be used for processing such as searching and sorting of information into classes and for information content determining processing, there has also been proposed a method using a learning result obtained by a learning method called learning vector quantization (LVQ) which is, again, the supervised learning (for example, see the above document (Tokutaka); and Yahagi et al., “Neural Network and Fuzzy Signal Processing,” Corona Publishing Co., Ltd., 1998, pp. 41-47). In the learning method based on this learning vector quantization, learning is performed by repeating the following steps. First, a reference feature vector group including a plurality of reference feature vectors of which corresponding classes are known is prepared. Thereafter, a feature vector as a learning target, of which corresponding class is also known, is sequentially read and a winner vector having the highest similarity to the feature vector as the learning target is retrieved from the reference feature vector group. Accordingly, when a corresponding class associated with the winner vector is identical to a corresponding class associated with the feature vector as the learning target, the winner vector is modified so as to increase the similarity to the feature vector as the learning target. On the other hand, when the two corresponding classes are not identical to each other, the winner vector is modified so as to decrease the similarity to the feature vector as the learning target. By use of the reference feature vector group obtained after the learning, a winner vector having the highest similarity to a vector indicating features of input information can be retrieved from the reference feature vector group and a class associated with the winner vector can be referred to. Thus, it is made possible to determine which one of the classes the input information belongs to. This learning vector quantization is similar to the above-described learning method using the self-organizing map based on the modified counterpropagation in that the learning vector quantization is the data learning method based on the supervised learning using the feature vectors. However, the learning vector quantization is different from the learning method using the self-organizing map at the point that there is no spatial interaction in learning by the reference feature vector group. Specifically, as described above, in the learning method using the self-organizing map, conceptually, the reference feature vectors are spatially arranged on the self-organizing map and each step of learning affects not only the winner vector but also reference feature vectors distributed in the vicinity of the winner vector. However, in the method based on the learning vector quantization, the vectors are not spatially arranged within the reference feature vector group and vicinity learning is not performed.
As described above, by use of the self-organizing map obtained after learning, in which the respective vector points and the contents (classes) of information are associated with each other, the information content determining processing can be performed. As the data learning method for deriving the self-organizing map, the method based on the modified counterpropagation has been proposed. However, in the information content determining processing using the self-organizing map obtained after learning by this method, an erroneous determination is likely to occur particularly when a reference feature vector specified as a winner vector is a reference feature vector near a boundary between classes. For example, assume that a number of feature vectors known to belong to any of classes A, B and C are learned by use of the modified counterpropagation method and, as a result, a self-organizing map and a map of corresponding classes as schematically shown in FIG. 26 are obtained. On the self-organizing map obtained after learning, the reference feature vectors similar to each other are arranged in the positions close to each other as described above. However, the similarity between reference feature vectors within a region corresponding to each class tends to be particularly high in the vicinity of the center of each region and low in the vicinity of a boundary between classes. Therefore, for example, considered is a case where a winner vector for a vector expressing features of input information as a target of the content (class) determining processing is a reference feature vector in the vicinity of the center of a region corresponding to the class B, such as the vector V1 shown in FIG. 26. In this case, reliability of a determined result that “the class of the input information is B” is relatively high. However, when the winner vector is a reference feature vector in the vicinity of the boundary between classes, such as the vector V2, the reliability of the determined result is low and accuracy of the content determining processing is limited. This means that the accuracy is also lowered in meaning determination for an image in which feature vectors indicating features of an image, of which meaning is known, are learned and meaning determining processing for the image is performed. Besides the above, in the learning method based on the modified counterpropagation, learning contents at a stage before a self-organizing map is converged, that is, at an early learning stage where reference feature vectors on the map are still in a random state are reflected in a final self-organizing map. The above fact also becomes a factor for lowering the accuracy of later content determining processing. In particular, for example, when the number of learning samples is small, the accuracy is more likely to be lowered.
Meanwhile, in the learning vector quantization method that is another learning method applicable to the information content determining processing, as described above, it is necessary to prepare a reference feature vector group beforehand by selecting a suitable number of initial reference feature vectors for each of the classes. In the learning vector quantization, it is particularly important how to decide component values of these initial reference feature vectors and “the suitable number” for each of the classes. This is because of the following reason. In the learning vector quantization, the concept of the vicinity learning is not adopted unlike the learning method using the self-organizing map and the respective reference feature vectors are subjected to learning independently of each other (that is, modified independently). Thus, “the suitable number” of the reference feature vectors for each class, which is initially decided, remains throughout the learning. Moreover, depending on how to decide the initial component values, reference feature vectors on which no learning is performed or learning is hardly performed may exist. However, in reality, it is not easy to suitably decide the component values of the initial reference feature vectors and “the suitable number” for each class. As a result, the above fact becomes a factor for limited accuracy in the content determining processing using a learning result based on the learning vector quantization.